{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7f085718-4204-45b0-a268-6d4df51d81ef",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# The Stereology module\n",
    "\n",
    "TODO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "315f5820-a286-4277-a4a5-e1a24f6940d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "234ad2dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "module plot imported\n",
      "module averages imported\n",
      "module stereology imported\n",
      "module piezometers imported\n",
      "module template imported\n",
      "\n",
      "======================================================================================\n",
      "Welcome to GrainSizeTools script\n",
      "======================================================================================\n",
      "A free open-source cross-platform script to visualize and characterize grain size\n",
      "population and estimate differential stress via paleopizometers.\n",
      "\n",
      "Version: 2024.02.xx\n",
      "Documentation: https://marcoalopez.github.io/GrainSizeTools/\n",
      "\n",
      "Type get.functions_list() to get a list of the main methods\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# run the script (change the path to GrainSizeTools_script.py accordingly!)\n",
    "%run C:/Users/marco/Documents/GitHub/GrainSizeTools/grain_size_tools/GrainSizeTools_script.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a0cb713e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======================================\n",
      "volume fraction (up to 50 microns) = 41.65 %\n",
      "=======================================\n",
      "=======================================\n",
      "bin size = 14.24\n",
      "=======================================\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 1000x400 with 2 Axes>,\n",
       " (<Axes: xlabel='diameter ($\\\\mu m$)', ylabel='density'>,\n",
       "  <Axes: xlabel='diameter ($\\\\mu m$)', ylabel='cumulative volume (%)'>))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import the example dataset\n",
    "filepath = 'C:/Users/marco/Documents/GitHub/GrainSizeTools/grain_size_tools/DATA/data_set.txt'\n",
    "dataset = pd.read_csv(filepath, sep='\\t')\n",
    "\n",
    "# estimate equivalent circular diameters\n",
    "dataset['diameters'] = 2 * np.sqrt(dataset['Area'] / np.pi)\n",
    "\n",
    "# apply the Saltykov method from stereology module\n",
    "stereology.Saltykov(dataset['diameters'], numbins=11, calc_vol=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8c9b01c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;31mSignature:\u001b[0m\n",
      "\u001b[0mstereology\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSaltykov\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mdiameters\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mnumbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mcalc_vol\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mtext_file\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mreturn_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mleft_edge\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mDocstring:\u001b[0m\n",
      "Estimate the actual (3D) distribution of grain size from the population\n",
      "of apparent diameters measured in a thin section using a Saltykov-type\n",
      "algorithm (Saltykov 1967; Sahagian and Proussevitch 1998).\n",
      "\n",
      "The Saltykov method is optimal to estimate the volume of a particular grain\n",
      "size fraction as well as to obtain a qualitative view of the appearance of\n",
      "the actual 3D grain size population, either in uni- or multimodal populations.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "diameters : array_like\n",
      "    the apparent diameters of the grains.\n",
      "\n",
      "numbins : positive integer, optional\n",
      "    the number of bins/classes of the histogram. If not declared,\n",
      "    is set to 10 by default.\n",
      "\n",
      "calc_vol : positive scalar or None, optional\n",
      "    if the user specifies a diameter, the function will return the volume\n",
      "    occupied by the grain fraction up to that diameter.\n",
      "\n",
      "text_file : string or None, optional\n",
      "    if the user specifies a name, the function will store a csv file\n",
      "    with that name containing the Saltykov output.\n",
      "\n",
      "return_data : bool, optional\n",
      "   if True the function will return the position of the midpoints and\n",
      "   the frequencies.\n",
      "\n",
      "left_edge : positive scalar or 'min', optional\n",
      "    set the left edge of the histogram. Default is zero.\n",
      "\n",
      "Call functions\n",
      "--------------\n",
      "- unfold_population\n",
      "- Saltykov_plot\n",
      "\n",
      "Examples\n",
      "--------\n",
      ">>> Saltykov(diameters)\n",
      ">>> Saltykov(diameters, numbins=16, calc_vol=40)\n",
      ">>> Saltykov(diameters, text_file='foo.csv')\n",
      ">>> mid_points, frequencies = Saltykov(diameters, return_data=True)\n",
      "\n",
      "References\n",
      "----------\n",
      "Saltykov SA (1967) http://doi.org/10.1007/978-3-642-88260-9_31\n",
      "Sahagian and Proussevitch (1998) https://doi.org/10.1016/S0377-0273(98)00043-2\n",
      "\n",
      "Return\n",
      "------\n",
      "Statistical descriptors, a plot, and/or a file with the data (optional)\n",
      "\u001b[1;31mFile:\u001b[0m      c:\\users\\marco\\documents\\github\\grainsizetools\\grain_size_tools\\stereology.py\n",
      "\u001b[1;31mType:\u001b[0m      function"
     ]
    }
   ],
   "source": [
    "stereology.Saltykov?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "77d82c7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.31536871e-03 2.17302235e-02 2.25631643e-02 1.45570771e-02\n",
      " 6.13586532e-03 2.24830266e-03 1.29306084e-03 3.60326809e-04\n",
      " 0.00000000e+00 0.00000000e+00 4.11071036e-05]\n"
     ]
    }
   ],
   "source": [
    "mid_points, densities = stereology.Saltykov(dataset['diameters'], numbins=11, return_data=True)\n",
    "print(densities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "46fe3ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07024449636922886"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(densities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c5fcd08b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.000000650312342"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrected_densities = densities * 14.236\n",
    "np.sum(corrected_densities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "595f8512",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class (midpoint): 7.12, volume: 1.9 %\n",
      "Class (midpoint): 21.35, volume: 30.9 %\n",
      "Class (midpoint): 35.59, volume: 32.1 %\n",
      "Class (midpoint): 49.83, volume: 20.7 %\n",
      "Class (midpoint): 64.06, volume: 8.7 %\n",
      "Class (midpoint): 78.30, volume: 3.2 %\n",
      "Class (midpoint): 92.53, volume: 1.8 %\n",
      "Class (midpoint): 106.77, volume: 0.5 %\n",
      "Class (midpoint): 121.01, volume: 0.0 %\n",
      "Class (midpoint): 135.24, volume: 0.0 %\n",
      "Class (midpoint): 149.48, volume: 0.1 %\n"
     ]
    }
   ],
   "source": [
    "for index, percent in enumerate(corrected_densities):\n",
    "    print(f'Class (midpoint): {mid_points[index]:.2f}, volume: {percent*100:.1f} %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f81608f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======================================\n",
      "PREDICTED OPTIMAL VALUES\n",
      "Number of classes: 11\n",
      "MSD (lognormal shape) = 1.63 ± 0.06\n",
      "Geometric mean (scale) = 36.05 ± 1.27\n",
      "=======================================\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 640x480 with 1 Axes>,\n",
       " <Axes: xlabel='diameter ($\\\\mu m$)', ylabel='freq. (per unit vol.)'>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stereology.calc_shape(dataset['diameters'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5d6bc6c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean = 40.62\n",
      "mode = 28.39\n"
     ]
    }
   ],
   "source": [
    "geo_mean = 36.05\n",
    "msd = 1.63\n",
    "mu = np.log(geo_mean)\n",
    "sigma = np.log(msd)\n",
    "\n",
    "amean = np.exp(mu + (sigma**2 / 2))\n",
    "mode = np.exp(mu - sigma**2)\n",
    "print(f'mean = {amean:.2f}')\n",
    "print(f'mode = {mode:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b81fb91a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "volume fraction = 70.6 %\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import lognorm\n",
    "\n",
    "# Create a lognormal distribution\n",
    "dist = lognorm(s=sigma, scale=geo_mean)\n",
    "\n",
    "# Calculate the volume fraction between sizes 75 and 25\n",
    "volume_fraction = dist.cdf(75) - dist.cdf(25)\n",
    "\n",
    "print(f'volume fraction = {volume_fraction * 100:.1f} %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "50a5b0f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function calc_shape in module stereology:\n",
      "\n",
      "calc_shape(diameters, class_range=(10, 20))\n",
      "    Approximates the shape of the actual (3D) distribution of grain size\n",
      "    from a population of apparent diameters measured in a thin section using\n",
      "    the two-step method (Lopez-Sanchez and Llana-Funez, 2016).\n",
      "    \n",
      "    The method only works properly for unimodal lognormal-like grain size\n",
      "    populations and returns the MSD (i.e. shape) and the geometric mean\n",
      "    (i.e. scale) values, which describe the lognormal population of grain sizes\n",
      "    at their original (linear) scale.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    diameters : array_like\n",
      "        the apparent diameters of the grains\n",
      "    \n",
      "    class_range : tupe or list with two values, optional\n",
      "        the range of classes considered. The algorithm will estimate the optimal\n",
      "        number of classes within the defined range. Default = (10, 20)\n",
      "    \n",
      "    \n",
      "    Call functions\n",
      "    --------------\n",
      "    - Saltykov,\n",
      "    - fit_log,\n",
      "    - log_function\n",
      "    - gen_xgrid\n",
      "    - twostep_plot\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> calc_shape(diameters)\n",
      "    >>> calc_shape(diameters, class_range=(12, 18))\n",
      "    >>> calc_shape(diameters, initial_guess=True)\n",
      "    \n",
      "    References\n",
      "    ----------\n",
      "    Saltykov SA (1967) http://doi.org/10.1007/978-3-642-88260-9_31\n",
      "    Sahagian and Proussevitch (1998) https://doi.org/10.1016/S0377-0273(98)00043-2\n",
      "    Lopez-Sanchez and Llana-Funez (2016) https://doi.org/10.1016/j.jsg.2016.10.008\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    A plot with an estimate of the actual (3D) grains size distribution and\n",
      "    several statistical parameters\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(stereology.calc_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5de41d31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notebook tested in 2024-02-07 using:\n",
      "Python 3.10.13 | packaged by Anaconda, Inc. | (main, Sep 11 2023, 13:15:57) [MSC v.1916 64 bit (AMD64)]\n",
      "Numpy 1.26.3\n",
      "Matplotlib 3.8.0\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "from datetime import date    \n",
    "today = date.today().isoformat()\n",
    "import matplotlib as mpl\n",
    "\n",
    "print(f'Notebook tested in {today} using:')\n",
    "print('Python', sys.version)\n",
    "print('Numpy', np.__version__)\n",
    "print('Matplotlib', mpl.__version__)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
